
%matplotlib inline
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from datetime import datetime

from sklearn.model_selection import train_test_split
from sklearn.impute import KNNImputer

# Importing Classifier Modules
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Perceptron
from sklearn.linear_model import SGDClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV

#to be abole to view all columns and rows
pd.set_option("display.max_columns", None)
pd.set_option("display.max_rows", None)

import warnings
warnings.filterwarnings("ignore")


df=pd.read_csv('Data for Cleaning & Modeling.csv',low_memory=False)
df.head(5)


df.columns = ['Interest_Rate', 'loanID', 'borID', 'Loan_Requested', 'Loan_Funded', 'Investor_portion', \
                'Number_of_Payments', 'Loan_Grade','loanSGrade','job', 'Years_Employed', 'Home_Ownership',\
                'Annual_Income', 'Verification', 'Issued_MonYY','reason_Loan', 'loan_Cat','loanTitle', 'zip_3',\
                'State', 'Debit_Payment_Ratio', 'Delinquency_Frequency','Credit_Earliest_MonYY','Inquiries_Count',\
                'Delinquency_Recency', 'Public_Record_Recency','Open_Credit_Line_Count', 'Derogatory_Public_Record_Count',\
                'Total_Credit','Credit_Utilization_Rate','Total_Credit_Line_Count','Initial_Status']

df.head(5)


df.shape

(400000, 32)


na_count=df.isna().sum()


df_na = pd.DataFrame(na_count)
df_na['NA_count'] = df_na
df_na['NA_%'] = (df_na['NA_count'] / df.shape[0])*100
df_na = df_na.iloc[:,1:3]
df_na


df[df.Loan_Requested.isnull()]


df = df.dropna(subset = ['Loan_Requested'],axis =0)


df=df.dropna(subset=['Interest_Rate'],axis=0)


df=df.drop(columns = ['reason_Loan', 'loanTitle','Delinquency_Recency', 'Public_Record_Recency'])


print("Loan_garde count:"+str(len(df.Loan_Grade.value_counts())))
print("Loan_subgarde count:"+str(len(df.loanSGrade.value_counts())))

Loan_garde count:7
Loan_subgarde count:35


df=df.drop(columns = ['loanID', 'borID','loanSGrade','zip_3'])


df=df.drop(columns = ['job'])


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 338989 entries, 0 to 399999
Data columns (total 23 columns):
 #   Column                          Non-Null Count   Dtype  
---  ------                          --------------   -----  
 0   Interest_Rate                   338989 non-null  object 
 1   Loan_Requested                  338989 non-null  object 
 2   Loan_Funded                     338989 non-null  object 
 3   Investor_portion                338989 non-null  object 
 4   Number_of_Payments              338989 non-null  object 
 5   Loan_Grade                      287123 non-null  object 
 6   Years_Employed                  324195 non-null  object 
 7   Home_Ownership                  287030 non-null  object 
 8   Annual_Income                   287238 non-null  float64
 9   Verification                    338989 non-null  object 
 10  Issued_MonYY                    338989 non-null  object 
 11  loan_Cat                        338989 non-null  object 
 12  State                           338989 non-null  object 
 13  Debit_Payment_Ratio             338989 non-null  float64
 14  Delinquency_Frequency           338989 non-null  float64
 15  Credit_Earliest_MonYY           338989 non-null  object 
 16  Inquiries_Count                 338989 non-null  float64
 17  Open_Credit_Line_Count          338989 non-null  float64
 18  Derogatory_Public_Record_Count  338989 non-null  float64
 19  Total_Credit                    338989 non-null  float64
 20  Credit_Utilization_Rate         338765 non-null  object 
 21  Total_Credit_Line_Count         338989 non-null  float64
 22  Initial_Status                  338989 non-null  object 
dtypes: float64(8), object(15)
memory usage: 62.1+ MB


#Interest Rate on the loan
df['Interest_Rate'] =df['Interest_Rate'].str.replace("%","") 
df['Interest_Rate'] = df['Interest_Rate'].astype(float)
#df.Interest_Rate = df.Interest_Rate.replace('(%)','', regex = True).astype(float)


sns.distplot(df['Interest_Rate'])
plt.title("Distribution of Interest Rate")
plt.xlabel("Interest Rate in %")
plt.ylabel("Occurance in %")
plt.show()


df.Loan_Requested = df.Loan_Requested.replace('[\$\,]','', regex = True).astype(int)
df.Loan_Funded = df.Loan_Funded.replace('[\$\,]','', regex = True).astype(int)
df.Investor_portion = df.Investor_portion.replace('[\$\,]','', regex = True).astype(int)


#loan_requested
print(df['Loan_Requested'].describe())
sns.distplot(df[df['Loan_Requested'].notnull()]['Loan_Requested'])

count    338989.000000
mean      14276.968353
std        8252.264104
min         500.000000
25%        8000.000000
50%       12000.000000
75%       20000.000000
max       35000.000000
Name: Loan_Requested, dtype: float64

<AxesSubplot:xlabel='Loan_Requested', ylabel='Density'>


#loan_funded
print(df['Loan_Funded'].describe())
sns.distplot(df[df['Loan_Funded'].notnull()]['Loan_Funded'])

count    338989.000000
mean      14248.306140
std        8238.064335
min         500.000000
25%        8000.000000
50%       12000.000000
75%       20000.000000
max       35000.000000
Name: Loan_Funded, dtype: float64

<AxesSubplot:xlabel='Loan_Funded', ylabel='Density'>


#investor_portion
print(df['Investor_portion'].describe())
sns.distplot(df[df['Investor_portion'].notnull()]['Investor_portion'])

count    338989.000000
mean      14182.000445
std        8255.138412
min           0.000000
25%        8000.000000
50%       12000.000000
75%       19900.000000
max       35000.000000
Name: Investor_portion, dtype: float64

<AxesSubplot:xlabel='Investor_portion', ylabel='Density'>


cor=df.iloc[:,0:4]
heatmap = sns.heatmap(cor.corr(), vmin=-1, vmax=1, annot=True)
heatmap.set_title('Correlation Heatmap', fontdict={'fontsize':12}, pad=12);


df=df.drop(['Loan_Funded','Investor_portion'],axis=1)


sns.distplot(df['Loan_Requested'])
plt.title("Distribution and Skew of Loan_Requested")
plt.xlabel("Interest Rate in %")
plt.show()


df['Number_of_Payments'].value_counts()

 36 months    247791
 60 months     91198
Name: Number_of_Payments, dtype: int64


#create a category variable, based on Number_of_Payments
df['pay_36months'] = df['Number_of_Payments'].apply(lambda x: 1 if x == '36 months' else 0)


df = df.drop('Number_of_Payments', axis=1)


#df['Loan_Grade'].value_counts()
ax = sns.countplot(x=df['Loan_Grade'],order=df['Loan_Grade'].value_counts(ascending=False).index)
abs_values = df['Loan_Grade'].value_counts(ascending=False).values
ax.bar_label(container=ax.containers[0])
plt.show()


df["Loan_Grade"] = df["Loan_Grade"].fillna('Others').astype("category")


sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Loan_Grade'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

<seaborn.axisgrid.PairGrid at 0x7f7c84205040>


sns.set(rc={"figure.figsize":(10,6)})
sns.histplot(df[df["Years_Employed"].notnull()]["Years_Employed"])
plt.show()
df["Years_Employed"].describe()

count        324195
unique           11
top       10+ years
freq         108491
Name: Years_Employed, dtype: object


#clean the data form and 
df['Years_Employed']= df['Years_Employed'].str.replace('years','').str.replace('year','').str.replace('+','')
df['Years_Employed']= df['Years_Employed'].str.replace('< 1','0')
df['Years_Employed']= df['Years_Employed'].astype('float')


#plot the years employeed and interest rate
sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Years_Employed'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

<seaborn.axisgrid.PairGrid at 0x7f7c8890f850>


df['Years_Employed']=df['Years_Employed'].interpolate()


new=['0-4','5-9','10']
df['Years_Employed']= [new[int(x)//5] if x!='Other' else 'Other' for x in df['Years_Employed']]

print(df['Years_Employed'].value_counts())
print(df.groupby('Years_Employed')[['Years_Employed','Interest_Rate']].mean())

sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Years_Employed'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

0-4    128696
10     110301
5-9     99992
Name: Years_Employed, dtype: int64
                Interest_Rate
Years_Employed               
0-4                 13.744290
10                  14.143337
5-9                 13.988912

<seaborn.axisgrid.PairGrid at 0x7f7c87b75d90>


df['Home_Ownership'].value_counts()

MORTGAGE    145958
RENT        115958
OWN          24976
OTHER          107
NONE            30
ANY              1
Name: Home_Ownership, dtype: int64


df['Home_Ownership']=df['Home_Ownership'].str.replace('NONE','OTHER').str.replace('ANY','OTHER')
df['Home_Ownership']=df['Home_Ownership'].fillna("OTHER")
df['Home_Ownership'] = df['Home_Ownership'].astype("category")
df['Home_Ownership'].value_counts()

MORTGAGE    145958
RENT        115958
OTHER        52097
OWN          24976
Name: Home_Ownership, dtype: int64


sns.countplot(x='Home_Ownership',data=df)

<AxesSubplot:xlabel='Home_Ownership', ylabel='count'>


df.groupby('Home_Ownership')[['Home_Ownership','Interest_Rate']].mean()


df.shape

(338989, 21)


df['Annual_Income'].describe()

count    2.872380e+05
mean     7.315146e+04
std      5.618967e+04
min      3.000000e+03
25%      4.500000e+04
50%      6.300000e+04
75%      8.807875e+04
max      7.500000e+06
Name: Annual_Income, dtype: float64


sns.distplot(df[df['Annual_Income'].notnull()]['Annual_Income'])

<AxesSubplot:xlabel='Annual_Income', ylabel='Density'>


fig = px.box(df, x="Annual_Income")
fig.show()


print(len(df[df.Annual_Income>1526060]))

15


#only select which is smaller than 1526060
df= df.loc[~(df['Annual_Income']>1526060)]
df.shape

(338974, 21)


#input na value with interpolate, since it will input value with the nearest distance
df.Annual_Income=df.Annual_Income.interpolate()
df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 338974 entries, 0 to 399999
Data columns (total 21 columns):
 #   Column                          Non-Null Count   Dtype   
---  ------                          --------------   -----   
 0   Interest_Rate                   338974 non-null  float64 
 1   Loan_Requested                  338974 non-null  int64   
 2   Loan_Grade                      338974 non-null  category
 3   Years_Employed                  338974 non-null  object  
 4   Home_Ownership                  338974 non-null  category
 5   Annual_Income                   338974 non-null  float64 
 6   Verification                    338974 non-null  object  
 7   Issued_MonYY                    338974 non-null  object  
 8   loan_Cat                        338974 non-null  object  
 9   State                           338974 non-null  object  
 10  Debit_Payment_Ratio             338974 non-null  float64 
 11  Delinquency_Frequency           338974 non-null  float64 
 12  Credit_Earliest_MonYY           338974 non-null  object  
 13  Inquiries_Count                 338974 non-null  float64 
 14  Open_Credit_Line_Count          338974 non-null  float64 
 15  Derogatory_Public_Record_Count  338974 non-null  float64 
 16  Total_Credit                    338974 non-null  float64 
 17  Credit_Utilization_Rate         338750 non-null  object  
 18  Total_Credit_Line_Count         338974 non-null  float64 
 19  Initial_Status                  338974 non-null  object  
 20  pay_36months                    338974 non-null  int64   
dtypes: category(2), float64(9), int64(2), object(8)
memory usage: 52.4+ MB


sns.distplot(df['Annual_Income'])

<AxesSubplot:xlabel='Annual_Income', ylabel='Density'>


df.shape

(338974, 21)


#KNN to fill NAs
#https://medium.com/@kyawsawhtoon/a-guide-to-knn-imputation-95e2dc496e
#from sklearn.preprocessing import MinMaxScaler

#scaler = MinMaxScaler()
#df = pd.DataFrame(scaler.fit_transform(df), columns = df.columns)
#df.head()

#from sklearn.impute import KNNImputer
#imputer = KNNImputer(n_neighbors=5)
#df1 = pd.DataFrame(imputer.fit_transform(df1),columns = df1.Annual_Income)


df['Verification'].value_counts()
df['Verification'] = df['Verification'].astype("category")
df['Verification'].value_counts()

VERIFIED - income           127036
not verified                107868
VERIFIED - income source    104070
Name: Verification, dtype: int64


df["Verification"] = df["Verification"].str.replace("VERIFIED - income", "verified").str.replace("verified source","verified")
df['Verification'].value_counts()

verified        231106
not verified    107868
Name: Verification, dtype: int64


sns.set(style="whitegrid")
i = sns.PairGrid(df, x_vars = ['Verification'], y_vars = 'Interest_Rate', size = 5)
i.map(sns.pointplot, color = sns.xkcd_rgb['red'])
i.fig.set_size_inches(7,5)
i.set(ylim = (5,26));


# issued_date
df['issued_month'] = df['Issued_MonYY'].apply(lambda x: str(x)[:3])
df['issued_year'] = df['Issued_MonYY'].apply(lambda x: str(x)[-2:])

# credit_earliest date
df['credit_earliest_month'] = df['Credit_Earliest_MonYY'].apply(lambda x: str(x)[:3])
df['credit_earliest_year'] = df['Credit_Earliest_MonYY'].apply(lambda x: str(x)[-2:])

df = df.drop(['Issued_MonYY', 'Credit_Earliest_MonYY' ], axis=1)


df['issued_month'].unique()

array(['Aug', 'May', 'Mar', 'Nov', 'Apr', 'Sep', 'Feb', 'Dec', 'Jun',
       'Oct', 'Jul', 'Jan'], dtype=object)


df['issued_year'].unique()

array(['09', '08', '14', '10', '12', '07', '11', '13'], dtype=object)


df['credit_earliest_month'].unique()

array(['Feb', 'Oct', 'Jun', 'Jan', 'Dec', 'Apr', 'Nov', 'Jul', 'May',
       'Aug', 'Sep', 'Mar'], dtype=object)


df['credit_earliest_year'].unique()

array(['94', '00', '85', '96', '98', '93', '01', '06', '95', '97', '90',
       '91', '03', '05', '75', '02', '88', '86', '87', '92', '89', '99',
       '04', '83', '80', '84', '71', '63', '81', '76', '82', '79', '72',
       '78', '73', '77', '74', '69', '70', '68', '67', '64', '07', '61',
       '62', '66', '65', '54', '08', '50', '46', '59', '56', '09', '58',
       '60', '10', '55', '53', '57', '11', '44', '51', '49'], dtype=object)


# Define month convertion function
def month(x):
    months = {
        "jan": 1,
        "feb": 2,
        "mar": 3,
        "apr": 4,
        "may": 5,
        "jun": 6,
        "jul": 7,
        "aug": 8,
        "sep": 9,
        "oct": 10,
        "nov": 11,
        "dec": 12,
    }
    try: # convert all the month into lowercase to match them to numbers
        x = months[x.strip()[:3].lower()]
        return x
    except:
        pass

# Define year convertion function
# Since it's only 2023 now, I assume that last 2 digits greater than 30 is in 1900 century
# for last 2 digits less than 30, I assume that it's in 2000 century

def year(x):
    x = x.strip()[-2:]
    try:
        if float(x) > 30: 
            x = "19" + str(x)
        else:             # 
            x = "20" + str(x)
        return int(x)
    except:
        pass


# change issued_date & credit_earliest_date's Month & Year
df['issued_month'] = df['issued_month'].apply(lambda x: month(x))
df['credit_earliest_month']  = df['credit_earliest_month'] .apply(lambda x: month(x))
df['issued_year'] = df['issued_year'].apply(lambda x: year(x))
df['credit_earliest_year'] = df['credit_earliest_year'].apply(lambda x: year(x))


countplt, ax = plt.subplots(figsize = (10,7))

ax = sns.countplot(x='issued_month', 
              data=df,
             order = df['issued_month'].value_counts().index)


countplt, ax = plt.subplots(figsize = (10,7))

ax = sns.countplot(x='issued_year', 
              data=df,
             order = df['issued_year'].value_counts().index)


countplt, ax = plt.subplots(figsize = (10,7))

ax = sns.countplot(x='credit_earliest_month', 
              data=df,
             order = df['credit_earliest_month'].value_counts().index)


countplt, ax = plt.subplots(figsize = (10,7))

ax = sns.countplot(x='credit_earliest_year', 
              data=df,
             order = df['credit_earliest_year'].value_counts().index[:10])


df['Initial_Status'].value_counts()

f    232588
w    106386
Name: Initial_Status, dtype: int64


sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Initial_Status'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

<seaborn.axisgrid.PairGrid at 0x7f7ca4a50e80>


df['loan_Cat'].value_counts()
#punc = ['major_purchase','small_business','car','medical','moving','wedding','vacation',]
#for i in punc:
#    a = [x.replace(i,'') for x in a]

debt_consolidation    198217
credit_card            75678
home_improvement       19622
other                  17154
major_purchase          7312
small_business          5359
car                     4115
medical                 3328
moving                  2138
wedding                 1934
vacation                1848
house                   1723
educational              279
renewable_energy         267
Name: loan_Cat, dtype: int64


sns.set(style="whitegrid")
i = sns.PairGrid(df, x_vars = ['loan_Cat'], y_vars = 'Interest_Rate', size = 5,
                hue_order= df['Interest_Rate'])
i.map(sns.pointplot, color = sns.xkcd_rgb['red'])
i.fig.set_size_inches(15,5)
for ax in i.axes.flat:
    plt.setp(ax.get_xticklabels(), rotation=90)
i.set(ylim = (5,26));


#change satet into 5 regions in US
states = {
    "AK": "Other",
    "AL": "South",
    "AR": "South",
    "AS": "Other",
    "AZ": "West",
    "CA": "West",
    "CO": "West",
    "CT": "Northeast",
    "DC": "Northeast",
    "DE": "Northeast",
    "FL": "South",
    "GA": "South",
    "GU": "Other",
    "HI": "Other",
    "IA": "Midwest",
    "ID": "West",
    "IL": "Midwest",
    "IN": "Midwest",
    "KS": "Midwest",
    "KY": "South",
    "LA": "South",
    "MA": "Northeast",
    "MD": "Northeast",
    "ME": "Northeast",
    "MI": "West",
    "MN": "Midwest",
    "MO": "Midwest",
    "MP": "Other",
    "MS": "South",
    "MT": "West",
    "NA": "Other",
    "NC": "South",
    "ND": "Midwest",
    "NE": "West",
    "NH": "Northeast",
    "NJ": "Northeast",
    "NM": "West",
    "NV": "West",
    "NY": "Northeast",
    "OH": "Midwest",
    "OK": "South",
    "OR": "West",
    "PA": "Northeast",
    "PR": "Other",
    "RI": "Northeast",
    "SC": "South",
    "SD": "Midwest",
    "TN": "South",
    "TX": "South",
    "UT": "West",
    "VA": "South",
    "VI": "Other",
    "VT": "Northeast",
    "WA": "West",
    "WI": "Midwest",
    "WV": "South",
    "WY": "West",
}


df["Region"] = df["State"].map(states)

df = df.drop("State", axis=1)


df["Region"] = df["Region"].astype("category")


df['Region'].value_counts()

South        107299
West          99157
Northeast     81302
Midwest       48471
Other          2745
Name: Region, dtype: int64


#df1.groupby('State')[['State','Interest_Rate']].mean()
df.groupby('Region')[['Region','Interest_Rate']].mean()
#the interest rate in west is slightly lower than other region


#visualization on the 
sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Region'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

<seaborn.axisgrid.PairGrid at 0x7f7c92b884f0>


sns.distplot(df[['Debit_Payment_Ratio']]['Debit_Payment_Ratio'],bins = 10);
df['Debit_Payment_Ratio'].describe()

count    338974.000000
mean         16.997916
std           7.696701
min           0.000000
25%          11.250000
50%          16.700000
75%          22.500000
max          39.990000
Name: Debit_Payment_Ratio, dtype: float64


df['Delinquency_Frequency'].value_counts()

0.0     280238
1.0      39793
2.0      11493
3.0       3964
4.0       1658
5.0        841
6.0        441
7.0        226
8.0        111
9.0         68
10.0        48
11.0        27
12.0        23
13.0        13
18.0         6
14.0         6
16.0         5
15.0         4
17.0         3
19.0         2
24.0         1
29.0         1
22.0         1
21.0         1
Name: Delinquency_Frequency, dtype: int64


sns.distplot(df[['Delinquency_Frequency']]['Delinquency_Frequency']);
df['Delinquency_Frequency'].describe()

count    338974.000000
mean          0.274284
std           0.779301
min           0.000000
25%           0.000000
50%           0.000000
75%           0.000000
max          29.000000
Name: Delinquency_Frequency, dtype: float64


df['has_delinquency'] = df['Delinquency_Frequency'].apply(lambda x: 1 if x != 0 else 0)
df['has_delinquency']=df['has_delinquency'].astype("category")
df = df.drop(columns = ['Delinquency_Frequency'])


df['has_delinquency'].value_counts()

0    280238
1     58736
Name: has_delinquency, dtype: int64


df['Open_Credit_Line_Count'].describe()

count    338974.000000
mean         11.117991
std           4.896049
min           0.000000
25%           8.000000
50%          10.000000
75%          14.000000
max          76.000000
Name: Open_Credit_Line_Count, dtype: float64


#df.hist(column = 'Open_Credit_Line_Count')
sns.distplot(df['Open_Credit_Line_Count'])

<AxesSubplot:xlabel='Open_Credit_Line_Count', ylabel='Density'>


df['log_Open_Credit_Line_Count'] = np.log10(df['Open_Credit_Line_Count'])
df['log_Open_Credit_Line_Count'] .replace([-np.inf], 0, inplace = True)
df=df.drop("Open_Credit_Line_Count", axis=1)
df.head(5)


sns.distplot(df['log_Open_Credit_Line_Count'])

<AxesSubplot:xlabel='log_Open_Credit_Line_Count', ylabel='Density'>


df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 338974 entries, 0 to 399999
Data columns (total 23 columns):
 #   Column                          Non-Null Count   Dtype   
---  ------                          --------------   -----   
 0   Interest_Rate                   338974 non-null  float64 
 1   Loan_Requested                  338974 non-null  int64   
 2   Loan_Grade                      338974 non-null  category
 3   Years_Employed                  338974 non-null  object  
 4   Home_Ownership                  338974 non-null  category
 5   Annual_Income                   338974 non-null  float64 
 6   Verification                    338974 non-null  object  
 7   loan_Cat                        338974 non-null  object  
 8   Debit_Payment_Ratio             338974 non-null  float64 
 9   Inquiries_Count                 338974 non-null  float64 
 10  Derogatory_Public_Record_Count  338974 non-null  float64 
 11  Total_Credit                    338974 non-null  float64 
 12  Credit_Utilization_Rate         338750 non-null  object  
 13  Total_Credit_Line_Count         338974 non-null  float64 
 14  Initial_Status                  338974 non-null  object  
 15  pay_36months                    338974 non-null  int64   
 16  issued_month                    338974 non-null  int64   
 17  issued_year                     338974 non-null  int64   
 18  credit_earliest_month           338974 non-null  int64   
 19  credit_earliest_year            338974 non-null  int64   
 20  Region                          338974 non-null  category
 21  has_delinquency                 338974 non-null  category
 22  log_Open_Credit_Line_Count      338974 non-null  float64 
dtypes: category(4), float64(8), int64(6), object(5)
memory usage: 53.0+ MB


df['Derogatory_Public_Record_Count'].value_counts()

0.0     296216
1.0      37292
2.0       3668
3.0       1076
4.0        346
5.0        186
6.0         89
7.0         47
8.0         19
9.0         11
10.0        10
11.0         3
13.0         2
49.0         1
54.0         1
17.0         1
34.0         1
21.0         1
63.0         1
40.0         1
18.0         1
14.0         1
Name: Derogatory_Public_Record_Count, dtype: int64


df.groupby('Derogatory_Public_Record_Count')[['Interest_Rate']].mean()


df['has_derogatory'] = df['Derogatory_Public_Record_Count'].apply(lambda x: 1 if x != 0 else 0)
df['has_derogatory']=df['has_derogatory'].astype("category")
df = df.drop(columns = ['Derogatory_Public_Record_Count'])


df['has_derogatory'].value_counts()

0    296216
1     42758
Name: has_derogatory, dtype: int64


df['Total_Credit'].describe()

count    3.389740e+05
mean     1.596715e+04
std      1.961100e+04
min      0.000000e+00
25%      6.462000e+03
50%      1.178150e+04
75%      2.023300e+04
max      2.568995e+06
Name: Total_Credit, dtype: float64


sns.distplot(df['Total_Credit'])

<AxesSubplot:xlabel='Total_Credit', ylabel='Density'>


fig = px.box(df, y="Total_Credit")
fig.show()


df = df[df['Total_Credit'] <= 40889]


df.shape

(323200, 23)


df['Credit_Utilization_Rate'] = df['Credit_Utilization_Rate'].str.replace("%","")
#for col in ["Credit_Utilization_Rate"]:
#    df[col] = df[col].str.slice(stop=-1).astype("float")

df['Credit_Utilization_Rate']= pd.to_numeric(df['Credit_Utilization_Rate'])
df['Credit_Utilization_Rate'].describe()

count    322998.000000
mean         55.811935
std          23.726311
min           0.000000
25%          38.900000
50%          57.200000
75%          74.300000
max         892.300000
Name: Credit_Utilization_Rate, dtype: float64


df['Credit_Utilization_Rate'].isna().sum()

202


mean_value=df['Credit_Utilization_Rate'].mean()


df.loc['Credit_Utilization_Rate']=df['Credit_Utilization_Rate'].fillna(value=mean_value, inplace=True)


df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 323201 entries, 0 to Credit_Utilization_Rate
Data columns (total 23 columns):
 #   Column                      Non-Null Count   Dtype  
---  ------                      --------------   -----  
 0   Interest_Rate               323200 non-null  float64
 1   Loan_Requested              323200 non-null  float64
 2   Loan_Grade                  323200 non-null  object 
 3   Years_Employed              323200 non-null  object 
 4   Home_Ownership              323200 non-null  object 
 5   Annual_Income               323200 non-null  float64
 6   Verification                323200 non-null  object 
 7   loan_Cat                    323200 non-null  object 
 8   Debit_Payment_Ratio         323200 non-null  float64
 9   Inquiries_Count             323200 non-null  float64
 10  Total_Credit                323200 non-null  float64
 11  Credit_Utilization_Rate     323200 non-null  float64
 12  Total_Credit_Line_Count     323200 non-null  float64
 13  Initial_Status              323200 non-null  object 
 14  pay_36months                323200 non-null  float64
 15  issued_month                323200 non-null  float64
 16  issued_year                 323200 non-null  float64
 17  credit_earliest_month       323200 non-null  float64
 18  credit_earliest_year        323200 non-null  float64
 19  Region                      323200 non-null  object 
 20  has_delinquency             323200 non-null  float64
 21  log_Open_Credit_Line_Count  323200 non-null  float64
 22  has_derogatory              323200 non-null  float64
dtypes: float64(16), object(7)
memory usage: 59.2+ MB


df['Total_Credit_Line_Count'].describe()

count    323200.000000
mean         24.633846
std          11.356222
min           2.000000
25%          16.000000
50%          23.000000
75%          31.000000
max         118.000000
Name: Total_Credit_Line_Count, dtype: float64


sns.distplot(df['Total_Credit_Line_Count'])

<AxesSubplot:xlabel='Total_Credit_Line_Count', ylabel='Density'>


fig = px.box(df, y="Total_Credit_Line_Count")
fig.show()


df = df[df['Total_Credit_Line_Count'] <= 53]
df.shape

(317178, 23)


df['Initial_Status'].value_counts()

f    218272
w     98906
Name: Initial_Status, dtype: int64


df.groupby('Initial_Status')[['Initial_Status','Interest_Rate']].mean()
#plot
sns.set(style="whitegrid")
g = sns.PairGrid(data=df, x_vars=['Initial_Status'], y_vars='Interest_Rate', size=5)
g.map(sns.pointplot, color=sns.xkcd_rgb["red"])

<seaborn.axisgrid.PairGrid at 0x7f7c92c1e640>


df['Initial_Status']=df['Initial_Status'].astype("category")


df.isna().sum()

Interest_Rate                 0
Loan_Requested                0
Loan_Grade                    0
Years_Employed                0
Home_Ownership                0
Annual_Income                 0
Verification                  0
loan_Cat                      0
Debit_Payment_Ratio           0
Inquiries_Count               0
Total_Credit                  0
Credit_Utilization_Rate       0
Total_Credit_Line_Count       0
Initial_Status                0
pay_36months                  0
issued_month                  0
issued_year                   0
credit_earliest_month         0
credit_earliest_year          0
Region                        0
has_delinquency               0
log_Open_Credit_Line_Count    0
has_derogatory                0
dtype: int64


#get dummies variables
df = pd.get_dummies(df, drop_first=True)
df.head()


df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 317178 entries, 0 to 399999
Data columns (total 47 columns):
 #   Column                       Non-Null Count   Dtype  
---  ------                       --------------   -----  
 0   Interest_Rate                317178 non-null  float64
 1   Loan_Requested               317178 non-null  float64
 2   Annual_Income                317178 non-null  float64
 3   Debit_Payment_Ratio          317178 non-null  float64
 4   Inquiries_Count              317178 non-null  float64
 5   Total_Credit                 317178 non-null  float64
 6   Credit_Utilization_Rate      317178 non-null  float64
 7   Total_Credit_Line_Count      317178 non-null  float64
 8   pay_36months                 317178 non-null  float64
 9   issued_month                 317178 non-null  float64
 10  issued_year                  317178 non-null  float64
 11  credit_earliest_month        317178 non-null  float64
 12  credit_earliest_year         317178 non-null  float64
 13  has_delinquency              317178 non-null  float64
 14  log_Open_Credit_Line_Count   317178 non-null  float64
 15  has_derogatory               317178 non-null  float64
 16  Loan_Grade_B                 317178 non-null  uint8  
 17  Loan_Grade_C                 317178 non-null  uint8  
 18  Loan_Grade_D                 317178 non-null  uint8  
 19  Loan_Grade_E                 317178 non-null  uint8  
 20  Loan_Grade_F                 317178 non-null  uint8  
 21  Loan_Grade_G                 317178 non-null  uint8  
 22  Loan_Grade_Others            317178 non-null  uint8  
 23  Years_Employed_10            317178 non-null  uint8  
 24  Years_Employed_5-9           317178 non-null  uint8  
 25  Home_Ownership_OTHER         317178 non-null  uint8  
 26  Home_Ownership_OWN           317178 non-null  uint8  
 27  Home_Ownership_RENT          317178 non-null  uint8  
 28  Verification_verified        317178 non-null  uint8  
 29  loan_Cat_credit_card         317178 non-null  uint8  
 30  loan_Cat_debt_consolidation  317178 non-null  uint8  
 31  loan_Cat_educational         317178 non-null  uint8  
 32  loan_Cat_home_improvement    317178 non-null  uint8  
 33  loan_Cat_house               317178 non-null  uint8  
 34  loan_Cat_major_purchase      317178 non-null  uint8  
 35  loan_Cat_medical             317178 non-null  uint8  
 36  loan_Cat_moving              317178 non-null  uint8  
 37  loan_Cat_other               317178 non-null  uint8  
 38  loan_Cat_renewable_energy    317178 non-null  uint8  
 39  loan_Cat_small_business      317178 non-null  uint8  
 40  loan_Cat_vacation            317178 non-null  uint8  
 41  loan_Cat_wedding             317178 non-null  uint8  
 42  Initial_Status_w             317178 non-null  uint8  
 43  Region_Northeast             317178 non-null  uint8  
 44  Region_Other                 317178 non-null  uint8  
 45  Region_South                 317178 non-null  uint8  
 46  Region_West                  317178 non-null  uint8  
dtypes: float64(16), uint8(31)
memory usage: 58.6+ MB


X, y = df.drop("Interest_Rate", axis=1), df["Interest_Rate"]


(x_train, x_test, y_train, y_test) = train_test_split( X, y, test_size=0.2, random_state=1)


# Import DecisionTreeRegressor from sklearn.tree
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import GradientBoostingRegressor

from sklearn.model_selection import cross_val_score
from sklearn.metrics import mean_squared_error as MSE


lr = LinearRegression()
# Fit 'linear' to the training set
lr.fit(x_train, y_train)

# Predict the test set labels 'y_pred'
lr_y_pred_test = lr.predict(x_test)
lr_y_pred_train = lr.predict(x_train)

lr_MSE_CV_scores = -cross_val_score(
    lr, x_train, y_train, cv=10, scoring="neg_mean_squared_error", n_jobs=-1
)
# Evaluate the test set RMSE
lr_rmse_train = MSE(y_train, lr_y_pred_train) ** (1 / 2)
lr_rmse_test = MSE(y_test, lr_y_pred_test) ** (1 / 2)
lr_RMSE_CV = (lr_MSE_CV_scores.mean()) ** (1 / 2)

# Print the test set RMSE
print("Train set RMSE of rf: {:.2f}".format(lr_rmse_train))
print("Test set RMSE of rf: {:.2f}".format(lr_rmse_test))
print("CV RMSE: {:.2f}".format(lr_RMSE_CV))

Train set RMSE of rf: 1.93
Test set RMSE of rf: 1.95
CV RMSE: 1.94


lr.score(x_train, y_train)

0.8029651645841578


lr.score(x_test, y_test)

0.8005115397963668


#feature importance
importances = pd.DataFrame(
    lr.coef_, columns=["Coefficients"], index=x_train.columns)
importances = importances.sort_values(by='Coefficients', ascending=False)[:10]

importances.plot(kind="barh", figsize=(9, 7))
plt.title("Ridge model")
plt.axvline(x=0, color=".5")
plt.subplots_adjust(left=0.3)


# Instantiate a random forests regressor 'rf' 400 estimators
rf = RandomForestRegressor(n_estimators=400, min_samples_leaf=0.1)
# Fit 'rf' to the training set
rf.fit(x_train, y_train)

# Predict the test set labels 'y_pred'
rf_y_pred_test = rf.predict(x_test)
rf_y_pred_train = rf.predict(x_train)
rf_MSE_CV_scores = -cross_val_score(
    rf, x_train, y_train, cv=10, scoring="neg_mean_squared_error", n_jobs=-1
)
# Evaluate the test set RMSE
rf_rmse_test = MSE(y_test, rf_y_pred_test) ** (1 / 2)
rf_rmse_train = MSE(y_train, rf_y_pred_train) ** (1 / 2)
rf_RMSE_CV = (rf_MSE_CV_scores.mean()) ** (1 / 2)

# Print the test set RMSE
print("Train set RMSE of rf: {:.2f}".format(rf_rmse_train))
print("Test set RMSE of rf: {:.2f}".format(rf_rmse_test))
print("CV RMSE: {:.2f}".format(rf_RMSE_CV))

Train set RMSE of rf: 3.85
Test set RMSE of rf: 3.85
CV RMSE: 3.85


rf.score(x_train, y_train)

0.21887540205491063


rf.score(x_test, y_test)

0.22096955023741294


importances_rf = pd.Series(rf.feature_importances_, index=X.columns)
# Sort importances_rf
sorted_importances_rf = importances_rf.sort_values(ascending=False).head()
# Make a horizontal bar plot
sorted_importances_rf.plot(kind="barh", color="blue")
plt.show()


dt = DecisionTreeRegressor(max_depth=15, min_samples_leaf=0.1, random_state=6)
# Fit dt to the training set
dt.fit(x_train, y_train)


# Predict the labels of the training set
dt_y_pred_train = dt.predict(x_train)
dt_y_pred_test = dt.predict(x_test)
dt_MSE_CV_scores = -cross_val_score(
    dt, x_train, y_train, cv=10, scoring="neg_mean_squared_error", n_jobs=-1
)

# Evaluate the training set RMSE of dt
dt_RMSE_train = (MSE(y_train, dt_y_pred_train)) ** (1 / 2)
dt_RMSE_test = (MSE(y_test, dt_y_pred_test)) ** (1 / 2)
dt_RMSE_CV = (dt_MSE_CV_scores.mean()) ** (1 / 2)

# Print RMSE score
print("Train RMSE: {:.2f}".format(dt_RMSE_train))
print("Test RMSE of dt: {:.2f}".format(dt_RMSE_test))
print("CV RMSE: {:.2f}".format(dt_RMSE_CV))

Train RMSE: 3.64
Test RMSE of dt: 3.64
CV RMSE: 3.64


dt.score(x_train, y_train)

0.3008469440519915


dt.score(x_test, y_test)

0.30341919246493654


dt1 = DecisionTreeRegressor(max_depth=40, min_samples_leaf=0.05, random_state=6)
# Fit dt to the training set
dt1.fit(x_train, y_train)

# Predict the labels 
dt1_y_pred_train = dt1.predict(x_train)
dt1_y_pred_test = dt1.predict(x_test)
# Compute the array containing the 10-folds CV MSEs
dt1_MSE_CV_scores = -cross_val_score(
    dt1, x_train, y_train, cv=10, scoring="neg_mean_squared_error", n_jobs=-1
)

# Evaluate the training set RMSE of dt
dt1_RMSE_train = (MSE(y_train, dt1_y_pred_train)) ** (1 / 2)
dt1_RMSE_test = (MSE(y_test, dt1_y_pred_test)) ** (1 / 2)
dt1_RMSE_CV = (dt1_MSE_CV_scores.mean()) ** (1 / 2)

# Print RMSE_train
print("Train RMSE: {:.2f}".format(dt1_RMSE_train))
print("Test RMSE of dt: {:.2f}".format(dt1_RMSE_test))
print("CV RMSE: {:.2f}".format(dt1_RMSE_CV))

Train RMSE: 2.98
Test RMSE of dt: 2.96
CV RMSE: 2.98


dt1.score(x_train, y_train)

0.5335649314897272


dt1.score(x_test, y_test)

0.539498205910132


gb = GradientBoostingRegressor(max_depth=10, n_estimators=200, random_state=2)
# Fit gb to the training set
gb.fit(x_train, y_train)

# Predict labels
gb_y_pred_train = gb.predict(x_train)
gb_y_pred_test = gb.predict(x_test)
# Compute the array containing the 10-folds CV MSEs
gb_MSE_CV_scores = -cross_val_score(
    dt1, x_train, y_train, cv=10, scoring="neg_mean_squared_error", n_jobs=-1
)


# Evaluate the RMSE of gb
gb_RMSE_train = (MSE(y_train, gb_y_pred_train)) ** (1 / 2)
gb_RMSE_test = (MSE(y_test, gb_y_pred_test)) ** (1 / 2)
# Compute the 10-folds CV RMSE
gb_RMSE_CV = (gb_MSE_CV_scores.mean()) ** (1 / 2)

# Print RMSE score
print("Train RMSE: {:.2f}".format(gb_RMSE_train))
print("Test RMSE of dt: {:.2f}".format(gb_RMSE_test))
print("CV RMSE: {:.2f}".format(gb_RMSE_CV))

Train RMSE: 1.09
Test RMSE of dt: 1.51
CV RMSE: 2.98


gb.score(x_train, y_train)

0.9369237979514815


gb.score(x_test, y_test)

0.8805048689825804


test = pd.read_csv('Holdout for Testing.csv')
#rename column name
test.columns = ['Interest_Rate', 'loanID', 'borID', 'Loan_Requested', 'Loan_Funded', 'Investor_portion', \
                'Number_of_Payments', 'Loan_Grade','loanSGrade','job', 'Years_Employed', 'Home_Ownership',\
                'Annual_Income', 'Verification', 'Issued_MonYY','reason_Loan', 'loan_Cat','loanTitle', 'zip_3',\
                'State', 'Debit_Payment_Ratio', 'Delinquency_Frequency','Credit_Earliest_MonYY','Inquiries_Count',\
                'Delinquency_Recency', 'Public_Record_Recency','Open_Credit_Line_Count', 'Derogatory_Public_Record_Count',\
                'Total_Credit','Credit_Utilization_Rate','Total_Credit_Line_Count','Initial_Status']

test.head(5)


#drop interest_rate in the test data
test = test.drop("Interest_Rate", axis=1)


test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 80000 entries, 0 to 79999
Data columns (total 31 columns):
 #   Column                          Non-Null Count  Dtype  
---  ------                          --------------  -----  
 0   loanID                          80000 non-null  int64  
 1   borID                           80000 non-null  int64  
 2   Loan_Requested                  80000 non-null  object 
 3   Loan_Funded                     80000 non-null  object 
 4   Investor_portion                80000 non-null  object 
 5   Number_of_Payments              80000 non-null  object 
 6   Loan_Grade                      80000 non-null  object 
 7   loanSGrade                      80000 non-null  object 
 8   job                             75606 non-null  object 
 9   Years_Employed                  75618 non-null  object 
 10  Home_Ownership                  80000 non-null  object 
 11  Annual_Income                   80000 non-null  float64
 12  Verification                    80000 non-null  object 
 13  Issued_MonYY                    80000 non-null  object 
 14  reason_Loan                     15 non-null     object 
 15  loan_Cat                        80000 non-null  object 
 16  loanTitle                       80000 non-null  object 
 17  zip_3                           80000 non-null  object 
 18  State                           80000 non-null  object 
 19  Debit_Payment_Ratio             80000 non-null  float64
 20  Delinquency_Frequency           80000 non-null  int64  
 21  Credit_Earliest_MonYY           80000 non-null  object 
 22  Inquiries_Count                 80000 non-null  int64  
 23  Delinquency_Recency             41296 non-null  float64
 24  Public_Record_Recency           13839 non-null  float64
 25  Open_Credit_Line_Count          80000 non-null  int64  
 26  Derogatory_Public_Record_Count  80000 non-null  int64  
 27  Total_Credit                    80000 non-null  int64  
 28  Credit_Utilization_Rate         79970 non-null  object 
 29  Total_Credit_Line_Count         80000 non-null  int64  
 30  Initial_Status                  80000 non-null  object 
dtypes: float64(4), int64(8), object(19)
memory usage: 18.9+ MB


# drop variables that are not predictive to target variable
test=test.drop(columns = ['reason_Loan', 'loanTitle','Delinquency_Recency', 'Public_Record_Recency'])
test=test.drop(columns = ['loanID', 'borID','loanSGrade','zip_3'])
test=test.drop(columns = ['job'])


#Loan_Requested, Loan_Funded,Investor_portion
test.Loan_Requested = test.Loan_Requested.replace('[\$\,]','', regex = True).astype(int)
test.Loan_Funded = test.Loan_Funded.replace('[\$\,]','', regex = True).astype(int)
test.Investor_portion = test.Investor_portion.replace('[\$\,]','', regex = True).astype(int)

test=test.drop(['Loan_Funded','Investor_portion'],axis=1)


# Number_of_Payments
test['pay_36months'] = test['Number_of_Payments'].apply(lambda x: 1 if x == '36 months' else 0)
test = test.drop('Number_of_Payments', axis=1)


#loan grade
test["Loan_Grade"] = test["Loan_Grade"].fillna('Others').astype("category")


#Years_Employed
test['Years_Employed']= test['Years_Employed'].str.replace('years','').str.replace('year','').str.replace('+','')
test['Years_Employed']= test['Years_Employed'].str.replace('< 1','0')
test['Years_Employed']= test['Years_Employed'].astype('float')

test['Years_Employed']=test['Years_Employed'].interpolate()
new=['0-4','5-9','10']
test['Years_Employed']= [new[int(x)//5] if x!='Other' else 'Other' for x in test['Years_Employed']]


#Home_Ownership
test['Home_Ownership']=test['Home_Ownership'].str.replace('NONE','OTHER').str.replace('ANY','OTHER')
test['Home_Ownership']=test['Home_Ownership'].fillna("OTHER")
test['Home_Ownership'] = test['Home_Ownership'].astype("category")


#Verification
test["Verification"] = test["Verification"].str.replace("VERIFIED - income", "verified").str.replace("verified source","verified")
test['Verification'] = test['Verification'].astype("category")


test = test.sort_values("Credit_Earliest_MonYY").reset_index()
test["Credit_Earliest_MonYY"][30000:35000]

30000     7-Jan
30001     7-Jan
30002     7-Jan
30003     7-Jan
30004     7-Jan
30005     7-Jan
30006     7-Jan
30007     7-Jan
30008     7-Jan
30009     7-Jan
30010     7-Jan
30011     7-Jan
30012     7-Jan
30013     7-Jan
30014     7-Jan
30015     7-Jan
30016     7-Jan
30017     7-Jan
30018     7-Jan
30019     7-Jan
30020     7-Jan
30021     7-Jan
30022     7-Jan
30023     7-Jan
30024     7-Jan
30025     7-Jan
30026     7-Jan
30027     7-Jan
30028     7-Jan
30029     7-Jan
30030     7-Jan
30031     7-Jan
30032     7-Jan
30033     7-Jan
30034     7-Jan
30035     7-Jan
30036     7-Jan
30037     7-Jan
30038     7-Jan
30039     7-Jan
30040     7-Jan
30041     7-Jan
30042     7-Jan
30043     7-Jan
30044     7-Jan
30045     7-Jan
30046     7-Jan
30047     7-Jan
30048     7-Jan
30049     7-Jan
30050     7-Jan
30051     7-Jan
30052     7-Jan
30053     7-Jan
30054     7-Jan
30055     7-Jan
30056     7-Jan
30057     7-Jan
30058     7-Jan
30059     7-Jan
30060     7-Jan
30061     7-Jan
30062     7-Jan
30063     7-Jul
30064     7-Jul
30065     7-Jul
30066     7-Jul
30067     7-Jul
30068     7-Jul
30069     7-Jul
30070     7-Jul
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34946    Apr-87
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34960    Apr-87
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34979    Apr-87
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34993    Apr-87
34994    Apr-87
34995    Apr-87
34996    Apr-87
34997    Apr-87
34998    Apr-87
34999    Apr-87
Name: Credit_Earliest_MonYY, dtype: object


# credit_earliest date
test["Credit_Earliest_MonYY"][:34017] = test["Credit_Earliest_MonYY"][:34017].apply(
    lambda x: datetime.strptime(str(x + "-22"), "%d-%b-%y"))

test["Credit_Earliest_MonYY"][34017:] = test["Credit_Earliest_MonYY"][34017:].apply(
    lambda x: datetime.strptime(str(x), "%b-%y"))


test['credit_earliest_month'] = pd.to_datetime(test['Credit_Earliest_MonYY']).dt.month
test['credit_earliest_year']  = pd.to_datetime(test['Credit_Earliest_MonYY']).dt.year
test['credit_earliest_year']  = test['credit_earliest_year'].apply(lambda x: str(x)[-2:]).astype("int")


# issued_date
test['issued_month'] = test['Issued_MonYY'].apply(lambda x: str(x)[-3:])
test['issued_year'] = test['Issued_MonYY'].apply(lambda x: str(x)[:2])

test['issued_month'] = test['issued_month'].apply(lambda x: month(x))
test['issued_year'] = test['issued_year'].apply(lambda x: year(x))
test['issued_year'] = test['issued_year'].apply(lambda x: str(x)[-2:]).astype("int")


test=test.drop(columns = ["Credit_Earliest_MonYY","Issued_MonYY","index"])


test.head()


test.isna().sum()

Loan_Requested                     0
Loan_Grade                         0
Years_Employed                     0
Home_Ownership                     0
Annual_Income                      0
Verification                       0
loan_Cat                           0
State                              0
Debit_Payment_Ratio                0
Delinquency_Frequency              0
Inquiries_Count                    0
Open_Credit_Line_Count             0
Derogatory_Public_Record_Count     0
Total_Credit                       0
Credit_Utilization_Rate           30
Total_Credit_Line_Count            0
Initial_Status                     0
pay_36months                       0
credit_earliest_month              0
credit_earliest_year               0
issued_month                       0
issued_year                        0
dtype: int64


#change State to Region
test["Region"] = test["State"].map(states)
test = test.drop("State", axis=1)


# change Delinquency_Frequency to has_delinquency
test['has_delinquency'] = test['Delinquency_Frequency'].apply(lambda x: 1 if x != 0 else 0)
test['has_delinquency']=test['has_delinquency'].astype("category")
test = test.drop(columns = ['Delinquency_Frequency'])


# change Open_Credit_Line_Count to log_Open_Credit_Line_Count
test['log_Open_Credit_Line_Count'] = np.log10(test['Open_Credit_Line_Count'])
test['log_Open_Credit_Line_Count'] .replace([-np.inf], 0, inplace = True)
test=test.drop("Open_Credit_Line_Count", axis=1)


#Derogatory_Public_Record_Count
test['has_derogatory'] = test['Derogatory_Public_Record_Count'].apply(lambda x: 1 if x != 0 else 0)
test['has_derogatory']=test['has_derogatory'].astype("category")
test = test.drop(columns = ['Derogatory_Public_Record_Count'])


# Credit_Utilization_Rate
test['Credit_Utilization_Rate'] = test['Credit_Utilization_Rate'].str.replace("%","")
test['Credit_Utilization_Rate']= pd.to_numeric(test['Credit_Utilization_Rate'])

test_mean_value=test['Credit_Utilization_Rate'].mean()
test.loc['Credit_Utilization_Rate']=test['Credit_Utilization_Rate'].fillna(value=test_mean_value, inplace=True)


# Total_Credit_Line_Count
#test = test[test['Total_Credit_Line_Count'] <= 53]


test.isna().sum()

Loan_Requested                1
Loan_Grade                    1
Years_Employed                1
Home_Ownership                1
Annual_Income                 1
Verification                  1
loan_Cat                      1
Debit_Payment_Ratio           1
Inquiries_Count               1
Total_Credit                  1
Credit_Utilization_Rate       1
Total_Credit_Line_Count       1
Initial_Status                1
pay_36months                  1
credit_earliest_month         1
credit_earliest_year          1
issued_month                  1
issued_year                   1
Region                        1
has_delinquency               1
log_Open_Credit_Line_Count    1
has_derogatory                1
dtype: int64


test.info()

<class 'pandas.core.frame.DataFrame'>
Index: 80001 entries, 0 to Credit_Utilization_Rate
Data columns (total 22 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   Loan_Requested              80000 non-null  float64
 1   Loan_Grade                  80000 non-null  object 
 2   Years_Employed              80000 non-null  object 
 3   Home_Ownership              80000 non-null  object 
 4   Annual_Income               80000 non-null  float64
 5   Verification                80000 non-null  object 
 6   loan_Cat                    80000 non-null  object 
 7   Debit_Payment_Ratio         80000 non-null  float64
 8   Inquiries_Count             80000 non-null  float64
 9   Total_Credit                80000 non-null  float64
 10  Credit_Utilization_Rate     80000 non-null  float64
 11  Total_Credit_Line_Count     80000 non-null  float64
 12  Initial_Status              80000 non-null  object 
 13  pay_36months                80000 non-null  float64
 14  credit_earliest_month       80000 non-null  float64
 15  credit_earliest_year        80000 non-null  float64
 16  issued_month                80000 non-null  float64
 17  issued_year                 80000 non-null  float64
 18  Region                      80000 non-null  object 
 19  has_delinquency             80000 non-null  float64
 20  log_Open_Credit_Line_Count  80000 non-null  float64
 21  has_derogatory              80000 non-null  float64
dtypes: float64(15), object(7)
memory usage: 14.0+ MB


# Get one-hot encoding
test = pd.get_dummies(test, drop_first=True)
test.head()


test.info()

<class 'pandas.core.frame.DataFrame'>
Index: 80001 entries, 0 to Credit_Utilization_Rate
Data columns (total 43 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   Loan_Requested               80000 non-null  float64
 1   Annual_Income                80000 non-null  float64
 2   Debit_Payment_Ratio          80000 non-null  float64
 3   Inquiries_Count              80000 non-null  float64
 4   Total_Credit                 80000 non-null  float64
 5   Credit_Utilization_Rate      80000 non-null  float64
 6   Total_Credit_Line_Count      80000 non-null  float64
 7   pay_36months                 80000 non-null  float64
 8   credit_earliest_month        80000 non-null  float64
 9   credit_earliest_year         80000 non-null  float64
 10  issued_month                 80000 non-null  float64
 11  issued_year                  80000 non-null  float64
 12  has_delinquency              80000 non-null  float64
 13  log_Open_Credit_Line_Count   80000 non-null  float64
 14  has_derogatory               80000 non-null  float64
 15  Loan_Grade_B                 80001 non-null  uint8  
 16  Loan_Grade_C                 80001 non-null  uint8  
 17  Loan_Grade_D                 80001 non-null  uint8  
 18  Loan_Grade_E                 80001 non-null  uint8  
 19  Loan_Grade_F                 80001 non-null  uint8  
 20  Loan_Grade_G                 80001 non-null  uint8  
 21  Years_Employed_10            80001 non-null  uint8  
 22  Years_Employed_5-9           80001 non-null  uint8  
 23  Home_Ownership_OWN           80001 non-null  uint8  
 24  Home_Ownership_RENT          80001 non-null  uint8  
 25  Verification_verified        80001 non-null  uint8  
 26  loan_Cat_credit_card         80001 non-null  uint8  
 27  loan_Cat_debt_consolidation  80001 non-null  uint8  
 28  loan_Cat_home_improvement    80001 non-null  uint8  
 29  loan_Cat_house               80001 non-null  uint8  
 30  loan_Cat_major_purchase      80001 non-null  uint8  
 31  loan_Cat_medical             80001 non-null  uint8  
 32  loan_Cat_moving              80001 non-null  uint8  
 33  loan_Cat_other               80001 non-null  uint8  
 34  loan_Cat_renewable_energy    80001 non-null  uint8  
 35  loan_Cat_small_business      80001 non-null  uint8  
 36  loan_Cat_vacation            80001 non-null  uint8  
 37  loan_Cat_wedding             80001 non-null  uint8  
 38  Initial_Status_w             80001 non-null  uint8  
 39  Region_Northeast             80001 non-null  uint8  
 40  Region_Other                 80001 non-null  uint8  
 41  Region_South                 80001 non-null  uint8  
 42  Region_West                  80001 non-null  uint8  
dtypes: float64(15), uint8(28)
memory usage: 11.9+ MB


test[test.Loan_Requested.isnull()]
test = test.dropna(subset = ['Loan_Requested'],axis =0)


#check the columns difference
col1 = list(test.columns)
col2 = list(df.columns)


for c in df.columns:
    if c not in test.columns:
        print(c)

Interest_Rate
Loan_Grade_Others
Home_Ownership_OTHER
loan_Cat_educational


# filling in the missing columns and fill in 0, 1 values according to the proportion in training set
test['Loan_Grade_Others'] = np.random.choice([0, 1], size=len(test), p=[0.85, 0.15])
test['Home_Ownership_OTHER'] = np.random.choice([0, 1], size=len(test), p=[0.84, 0.16])
test['loan_Cat_educational'] = np.random.choice([0, 1], size=len(test), p=[0.99, 0.01])


print(test.shape)
print(df.shape)

(80000, 46)
(317178, 47)


df.columns

Index(['Interest_Rate', 'Loan_Requested', 'Annual_Income',
       'Debit_Payment_Ratio', 'Inquiries_Count', 'Total_Credit',
       'Credit_Utilization_Rate', 'Total_Credit_Line_Count', 'pay_36months',
       'issued_month', 'issued_year', 'credit_earliest_month',
       'credit_earliest_year', 'has_delinquency', 'log_Open_Credit_Line_Count',
       'has_derogatory', 'Loan_Grade_B', 'Loan_Grade_C', 'Loan_Grade_D',
       'Loan_Grade_E', 'Loan_Grade_F', 'Loan_Grade_G', 'Loan_Grade_Others',
       'Years_Employed_10', 'Years_Employed_5-9', 'Home_Ownership_OTHER',
       'Home_Ownership_OWN', 'Home_Ownership_RENT', 'Verification_verified',
       'loan_Cat_credit_card', 'loan_Cat_debt_consolidation',
       'loan_Cat_educational', 'loan_Cat_home_improvement', 'loan_Cat_house',
       'loan_Cat_major_purchase', 'loan_Cat_medical', 'loan_Cat_moving',
       'loan_Cat_other', 'loan_Cat_renewable_energy',
       'loan_Cat_small_business', 'loan_Cat_vacation', 'loan_Cat_wedding',
       'Initial_Status_w', 'Region_Northeast', 'Region_Other', 'Region_South',
       'Region_West'],
      dtype='object')


test.columns

Index(['Loan_Requested', 'Annual_Income', 'Debit_Payment_Ratio',
       'Inquiries_Count', 'Total_Credit', 'Credit_Utilization_Rate',
       'Total_Credit_Line_Count', 'pay_36months', 'credit_earliest_month',
       'credit_earliest_year', 'issued_month', 'issued_year',
       'has_delinquency', 'log_Open_Credit_Line_Count', 'has_derogatory',
       'Loan_Grade_B', 'Loan_Grade_C', 'Loan_Grade_D', 'Loan_Grade_E',
       'Loan_Grade_F', 'Loan_Grade_G', 'Years_Employed_10',
       'Years_Employed_5-9', 'Home_Ownership_OWN', 'Home_Ownership_RENT',
       'Verification_verified', 'loan_Cat_credit_card',
       'loan_Cat_debt_consolidation', 'loan_Cat_home_improvement',
       'loan_Cat_house', 'loan_Cat_major_purchase', 'loan_Cat_medical',
       'loan_Cat_moving', 'loan_Cat_other', 'loan_Cat_renewable_energy',
       'loan_Cat_small_business', 'loan_Cat_vacation', 'loan_Cat_wedding',
       'Initial_Status_w', 'Region_Northeast', 'Region_Other', 'Region_South',
       'Region_West', 'Loan_Grade_Others', 'Home_Ownership_OTHER',
       'loan_Cat_educational'],
      dtype='object')


col=['Loan_Requested', 'Annual_Income',
       'Debit_Payment_Ratio', 'Inquiries_Count', 'Total_Credit',
       'Credit_Utilization_Rate', 'Total_Credit_Line_Count', 'pay_36months',
       'issued_month', 'issued_year', 'credit_earliest_month',
       'credit_earliest_year', 'has_delinquency', 'log_Open_Credit_Line_Count',
       'has_derogatory', 'Loan_Grade_B', 'Loan_Grade_C', 'Loan_Grade_D',
       'Loan_Grade_E', 'Loan_Grade_F', 'Loan_Grade_G', 'Loan_Grade_Others',
       'Years_Employed_10', 'Years_Employed_5-9', 'Home_Ownership_OTHER',
       'Home_Ownership_OWN', 'Home_Ownership_RENT', 'Verification_verified',
       'loan_Cat_credit_card', 'loan_Cat_debt_consolidation',
       'loan_Cat_educational', 'loan_Cat_home_improvement', 'loan_Cat_house',
       'loan_Cat_major_purchase', 'loan_Cat_medical', 'loan_Cat_moving',
       'loan_Cat_other', 'loan_Cat_renewable_energy',
       'loan_Cat_small_business', 'loan_Cat_vacation', 'loan_Cat_wedding',
       'Initial_Status_w', 'Region_Northeast', 'Region_Other', 'Region_South',
       'Region_West']


test = test[col]


test_pred = lr.predict(test)

test = pd.read_csv("Holdout for Testing.csv")
predict = pd.DataFrame(
    {"Loan_Id": test["X2"], "Predicted_Interest_Rate": test_pred}
)


# "Titanic Results from" *insert your name or UChicago net ID.
predict.to_csv("Loan_Results_YichinTzou.csv", index=False)

	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	X11	X12	X13	X14	X15	X16	X17	X18	X19	X20	X21	X23	X24	X25	X26	X27	X29	X30	X31	X32
0	11.89%	54734.0	80364.0	$25,000	$25,000	$19,080	36 months	B	B4	NaN	< 1 year	RENT	85000.0	VERIFIED - income	Aug-09	Due to a lack of personal finance education an...	debt_consolidation	Debt consolidation for on-time payer	941xx	CA	19.48	Feb-94	0.0	NaN	NaN	10.0	28854.0	52.10%	42.0	f
1	10.71%	55742.0	114426.0	$7,000	$7,000	$673	36 months	B	B5	CNN	< 1 year	RENT	65000.0	not verified	May-08	Just want to pay off the last bit of credit ca...	credit_card	Credit Card payoff	112xx	NY	14.29	Oct-00	0.0	NaN	NaN	7.0	33623.0	76.70%	7.0	f
2	16.99%	57167.0	137225.0	$25,000	$25,000	$24,725	36 months	D	D3	Web Programmer	1 year	RENT	70000.0	VERIFIED - income	Aug-14	Trying to pay a friend back for apartment brok...	debt_consolidation	mlue	100xx	NY	10.50	Jun-00	0.0	41.0	NaN	10.0	19878.0	66.30%	17.0	f
3	13.11%	57245.0	138150.0	$1,200	$1,200	$1,200	36 months	C	C2	city of beaumont texas	10+ years	OWN	54000.0	not verified	Mar-10	If funded, I would use this loan consolidate t...	debt_consolidation	zxcvb	777xx	TX	5.47	Jan-85	0.0	64.0	NaN	5.0	2584.0	40.40%	31.0	f
4	13.57%	57416.0	139635.0	$10,800	$10,800	$10,692	36 months	C	C3	State Farm Insurance	6 years	RENT	32000.0	not verified	Nov-09	I currently have a personal loan with Citifina...	debt_consolidation	Nicolechr1978	067xx	CT	11.63	Dec-96	1.0	58.0	NaN	14.0	3511.0	25.60%	40.0	f

	Interest_Rate	loanID	borID	Loan_Requested	Loan_Funded	Investor_portion	Number_of_Payments	Loan_Grade	loanSGrade	job	Years_Employed	Home_Ownership	Annual_Income	Verification	Issued_MonYY	reason_Loan	loan_Cat	loanTitle	zip_3	State	Debit_Payment_Ratio	Credit_Earliest_MonYY	Inquiries_Count	Delinquency_Recency	Public_Record_Recency	Open_Credit_Line_Count	Total_Credit	Credit_Utilization_Rate	Total_Credit_Line_Count	Initial_Status
0	11.89%	54734.0	80364.0	$25,000	$25,000	$19,080	36 months	B	B4	NaN	< 1 year	RENT	85000.0	VERIFIED - income	Aug-09	Due to a lack of personal finance education an...	debt_consolidation	Debt consolidation for on-time payer	941xx	CA	19.48	Feb-94	0.0	NaN	NaN	10.0	28854.0	52.10%	42.0	f
1	10.71%	55742.0	114426.0	$7,000	$7,000	$673	36 months	B	B5	CNN	< 1 year	RENT	65000.0	not verified	May-08	Just want to pay off the last bit of credit ca...	credit_card	Credit Card payoff	112xx	NY	14.29	Oct-00	0.0	NaN	NaN	7.0	33623.0	76.70%	7.0	f
2	16.99%	57167.0	137225.0	$25,000	$25,000	$24,725	36 months	D	D3	Web Programmer	1 year	RENT	70000.0	VERIFIED - income	Aug-14	Trying to pay a friend back for apartment brok...	debt_consolidation	mlue	100xx	NY	10.50	Jun-00	0.0	41.0	NaN	10.0	19878.0	66.30%	17.0	f
3	13.11%	57245.0	138150.0	$1,200	$1,200	$1,200	36 months	C	C2	city of beaumont texas	10+ years	OWN	54000.0	not verified	Mar-10	If funded, I would use this loan consolidate t...	debt_consolidation	zxcvb	777xx	TX	5.47	Jan-85	0.0	64.0	NaN	5.0	2584.0	40.40%	31.0	f
4	13.57%	57416.0	139635.0	$10,800	$10,800	$10,692	36 months	C	C3	State Farm Insurance	6 years	RENT	32000.0	not verified	Nov-09	I currently have a personal loan with Citifina...	debt_consolidation	Nicolechr1978	067xx	CT	11.63	Dec-96	1.0	58.0	NaN	14.0	3511.0	25.60%	40.0	f

	NA_count	NA_%
Interest_Rate	61010	15.25250
loanID	1	0.00025
borID	1	0.00025
Loan_Requested	1	0.00025
Loan_Funded	1	0.00025
Investor_portion	1	0.00025
Number_of_Payments	1	0.00025
Loan_Grade	61270	15.31750
loanSGrade	61270	15.31750
job	23986	5.99650
Years_Employed	17538	4.38450
Home_Ownership	61361	15.34025
Annual_Income	61028	15.25700
Verification	1	0.00025
Issued_MonYY	1	0.00025
reason_Loan	276440	69.11000
loan_Cat	1	0.00025
loanTitle	19	0.00475
zip_3	1	0.00025
State	1	0.00025
Debit_Payment_Ratio	1	0.00025
Delinquency_Frequency	1	0.00025
Credit_Earliest_MonYY	1	0.00025
Inquiries_Count	1	0.00025
Delinquency_Recency	218802	54.70050
Public_Record_Recency	348845	87.21125
Open_Credit_Line_Count	1	0.00025
Derogatory_Public_Record_Count	1	0.00025
Total_Credit	1	0.00025
Credit_Utilization_Rate	267	0.06675
Total_Credit_Line_Count	1	0.00025
Initial_Status	1	0.00025

	Interest_Rate	loanID	borID	Loan_Requested	Loan_Funded	Investor_portion	Number_of_Payments	Loan_Grade	loanSGrade	job	Years_Employed	Home_Ownership	Annual_Income	Verification	Issued_MonYY	reason_Loan	loan_Cat	loanTitle	zip_3	State	Debit_Payment_Ratio	Credit_Earliest_MonYY	Inquiries_Count	Delinquency_Recency	Public_Record_Recency	Open_Credit_Line_Count	Derogatory_Public_Record_Count	Total_Credit	Credit_Utilization_Rate	Total_Credit_Line_Count	Initial_Status
0	NaN	44409194	47416907	$6,000	$6,000	$6,000	36 months	C	C5	Electrician	10+ years	MORTGAGE	68000.0	VERIFIED - income	15-Mar	NaN	debt_consolidation	Debt consolidation	430xx	OH	28.31	2-Nov	1	26.0	NaN	18	0	19861	64.50%	33	f
1	NaN	44017917	47034722	$24,000	$24,000	$24,000	36 months	A	A1	Executive Assistant	8 years	RENT	110480.0	VERIFIED - income	15-Mar	NaN	debt_consolidation	Debt consolidation	945xx	CA	16.03	Dec-68	1	NaN	NaN	12	0	17001	26.20%	36	w
2	NaN	44259158	47306871	$35,000	$35,000	$35,000	36 months	C	C2	District Sales Leader	10+ years	MORTGAGE	86000.0	VERIFIED - income	15-Mar	NaN	debt_consolidation	Debt consolidation	195xx	PA	32.49	Oct-98	0	NaN	NaN	16	0	25797	49.90%	33	w
3	NaN	44429213	47476932	$10,000	$10,000	$10,000	60 months	D	D1	pharmacy associate	10+ years	RENT	30000.0	VERIFIED - income source	15-Mar	NaN	debt_consolidation	Debt consolidation	921xx	CA	32.96	Feb-99	1	NaN	114.0	13	1	9586	43.80%	21	w
4	NaN	44299188	47346901	$24,000	$24,000	$24,000	60 months	B	B1	Medical case manager	10+ years	MORTGAGE	82500.0	VERIFIED - income	15-Mar	NaN	debt_consolidation	Debt consolidation	196xx	PA	31.03	2-Dec	0	48.0	NaN	27	0	31842	41.30%	43	w

Loan Interest Rate Prediction¶

variables:¶

Import packages¶

1.Data cleaning and preprocessing¶

1.1 Load data¶

Rename column name¶

Mising value¶

1.2 Data Preprocessing & Exploratory Data Analysis¶

Interest Rate¶

Loan_Requested, Loan_Funded, Investor_portion¶

Number_of_Payments¶

Loan_grade¶

Process Years_Employed¶

Process Home ownership¶

Remove outlier for annual income¶

Process Verification to category¶

Issued time and credit earliest time, process the time to month, and year the request is issued, and years with credit¶

Initial status for the loan request¶

Loan category¶

State¶

Debit_Payment_Ratio¶

delinquency_frequency¶

Process Open_Credit_Line_Count¶

Process Derogatory_Public_Record_Count¶

Process Total_Credit¶

Process Credit_Utilization_Rate¶

Process Total_Credit_Line_Count¶

Initial status¶

1.3 One-hot encoding¶

2. Train/Test data split¶

3. Data modeling¶

3.1 linear Regression¶

3.2 Random forest¶

3.3 Decision tree¶

3.3.1 Decision tree parameter tuning: max_depth=40, min_samples_leaf=0.05¶

3.4 GradientBoostingRegressor¶

Apply to test data¶

apply all methods use in train data, to test data¶

Apply linear model¶

Summary¶

	Interest_Rate
Home_Ownership
MORTGAGE	13.662988
OTHER	13.985585
OWN	14.092569
RENT	14.253723

	Interest_Rate
Region
Midwest	13.942030
Northeast	13.938972
Other	14.446179
South	13.981531
West	13.902650

	Interest_Rate	Loan_Requested	Loan_Grade	Years_Employed	Home_Ownership	Annual_Income	Verification	loan_Cat	Debit_Payment_Ratio	Inquiries_Count	Total_Credit	Credit_Utilization_Rate	Total_Credit_Line_Count	Initial_Status	issued_month	issued_year	credit_earliest_month	credit_earliest_year	Region	log_Open_Credit_Line_Count
0	11.89	25000	B	0-4	RENT	85000.0	verified	debt_consolidation	19.48	0.0	28854.0	52.10%	42.0	f	8	2009	2	1994	West	1.000000
1	10.71	7000	B	0-4	RENT	65000.0	not verified	credit_card	14.29	0.0	33623.0	76.70%	7.0	f	5	2008	10	2000	Northeast	0.845098
2	16.99	25000	D	0-4	RENT	70000.0	verified	debt_consolidation	10.50	0.0	19878.0	66.30%	17.0	f	8	2014	6	2000	Northeast	1.000000
3	13.11	1200	C	10	OWN	54000.0	not verified	debt_consolidation	5.47	0.0	2584.0	40.40%	31.0	f	3	2010	1	1985	South	0.698970
4	13.57	10800	C	5-9	RENT	32000.0	not verified	debt_consolidation	11.63	1.0	3511.0	25.60%	40.0	f	11	2009	12	1996	Northeast	1.146128

	Interest_Rate
Derogatory_Public_Record_Count
0.0	13.810919
1.0	14.775732
2.0	15.631284
3.0	15.564907
4.0	15.963699
5.0	15.638763
6.0	15.097079
7.0	15.042766
8.0	16.267368
9.0	16.111818
10.0	14.169000
11.0	18.830000
13.0	15.455000
14.0	14.990000
17.0	15.610000
18.0	13.980000
21.0	16.240000
34.0	14.980000
40.0	23.430000
49.0	13.670000
54.0	15.310000
63.0	12.990000

	Interest_Rate	Loan_Requested	Annual_Income	Debit_Payment_Ratio	Inquiries_Count	Total_Credit	Credit_Utilization_Rate	Total_Credit_Line_Count	issued_month	issued_year	credit_earliest_month	credit_earliest_year	log_Open_Credit_Line_Count	Loan_Grade_B	Loan_Grade_C	Loan_Grade_D	Years_Employed_10	Years_Employed_5-9	Home_Ownership_OWN	Home_Ownership_RENT	Verification_verified	loan_Cat_credit_card	loan_Cat_debt_consolidation	Region_Northeast	Region_South	Region_West
0	11.89	25000.0	85000.0	19.48	0.0	28854.0	52.1	42.0	8.0	2009.0	2.0	1994.0	1.000000	1	0	0	0	0	0	1	1	0	1	0	0	1
1	10.71	7000.0	65000.0	14.29	0.0	33623.0	76.7	7.0	5.0	2008.0	10.0	2000.0	0.845098	1	0	0	0	0	0	1	0	1	0	1	0	0
2	16.99	25000.0	70000.0	10.50	0.0	19878.0	66.3	17.0	8.0	2014.0	6.0	2000.0	1.000000	0	0	1	0	0	0	1	1	0	1	1	0	0
3	13.11	1200.0	54000.0	5.47	0.0	2584.0	40.4	31.0	3.0	2010.0	1.0	1985.0	0.698970	0	1	0	1	0	1	0	0	0	1	0	1	0
4	13.57	10800.0	32000.0	11.63	1.0	3511.0	25.6	40.0	11.0	2009.0	12.0	1996.0	1.146128	0	1	0	0	1	0	1	0	0	1	1	0	0

	Loan_Requested	Loan_Grade	Years_Employed	Home_Ownership	Annual_Income	Verification	loan_Cat	State	Debit_Payment_Ratio	Delinquency_Frequency	Inquiries_Count	Open_Credit_Line_Count	Total_Credit	Credit_Utilization_Rate	Total_Credit_Line_Count	Initial_Status	credit_earliest_month	credit_earliest_year	issued_month	issued_year
0	30000	C	10	MORTGAGE	150000.0	verified	debt_consolidation	MD	14.24	0	1	17	20383	87.50%	34	w	4	22	2	15
1	12000	A	5-9	MORTGAGE	105000.0	not verified	credit_card	HI	8.53	1	0	8	30372	58%	28	f	4	22	2	15
2	20000	B	10	MORTGAGE	85000.0	verified	debt_consolidation	MA	17.52	0	0	7	18035	87.50%	18	w	4	22	2	15
3	9000	A	10	MORTGAGE	56000.0	verified	debt_consolidation	CA	6.24	2	0	11	5547	55.50%	16	w	4	22	2	15
4	11000	B	5-9	RENT	80000.0	not verified	debt_consolidation	CA	17.16	6	0	13	2651	39%	33	f	4	22	1	15